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The State of Artificial Intelligence in 2018: A Good Old Fashioned Report

Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.

We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world.

This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.

In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger informed conversation about the state of AI and its implication for the future.

We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.

Collaboratively produced in East London, UK by: Ian Hogarth and Nathan Benaich

@Liesl Yearsley Fully agree with your comment. If someone believes in evolution, then one must also acknowledge that AI can one day outsmart us. Whether or not that happens with the current versions of AI we have created, is a different debate.

Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. (Like: ...team is as strong as your weakest link...). So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born. This learning process dictates our outcome and that is fixed. There is no such thing as "Gut Feeling". It does not exist....me simply make it up to make any point heard across. So no matter how big or complex machines we build, it will only learn to behave by the "Rules" and the associated "Data", which always has a "fixed" outcome or result, same as ours. By laws of universe, without evolving, we would have remained as cave dwellers. So, every day of our life, we observe new rules and results which evolves us to the next level. But, If for example, I am locked up in a dark room, isolated from observing any new rules or data or results, I will be at the same level of intelligence as the day I get locked in. Similarly, if we cannot generate any new intelligence in isolation, we cannot feed the robots any new rules and hence they will remain at a certain level of intelligence for ever. What I am trying to say here is "...AI will never be more intelligent than its creator...".

The State of Artificial Intelligence in 2018: A Good Old Fashioned Report

1.
State of AI
June 29, 2018
#AIreportstateof.ai

2.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven
world.
This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in
the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to
trigger informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
- Research: Technology breakthroughs and their capabilities.
- Talent: Supply, demand and concentration of talent working in the field.
- Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
- Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
Nathan Benaich
@nathanbenaich
Ian Hogarth
@soundboy
#AIreport
stateof.ai 2018

3.
Nathan studied biology at Williams College and
earned a PhD from Cambridge in computational
and experimental cancer biology. He is an
investor in machine learning-driven technology
companies with his new firm, Air Street Capital,
and as a Venture Partner at Point Nine Capital.
He founded the RAAIS community and
Foundation to advance progress in AI.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
About the authors
Nathan Benaich Ian Hogarth
Ian studied engineering at Cambridge,
specialising in machine learning. His Masters
project was a computer vision system to classify
breast cancer biopsy images. He was co-founder
and CEO of Songkick, the concert service used
by 17 million music fans every month. He is an
angel investor in over 30 startups with a focus
on applied machine learning.
#AIreport
stateof.ai 2018

4.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Definitions
Artificial Intelligence (AI): A broad discipline with the goal of creating intelligent machines, as opposed to the
natural intelligence that is demonstrated by humans and animals. It has become a somewhat catch all term that
nonetheless captures the long term ambition of the field to build machines that emulate and then exceed the full
range of human cognition.
Machine learning (ML): A subset of AI that often uses statistical techniques to give machines the ability to "learn"
from data without being explicitly given the instructions for how to do so. This process is known as “training” a
“model” using a learning “algorithm” that progressively improves model performance on a specific task.
Reinforcement learning (RL): An area of ML that has received particular attention from the research community
over the past decade. It is concerned with software agents that learn goal-oriented behavior by trial and error in an
environment that provides rewards or penalties in response to the agent’s actions towards achieving that goal.
Deep learning (DL): An area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how
to recognise complex patterns in data. The “deep” in deep learning refers to the large number of layers of neurons
in contemporary ML models that help to learn rich representations of data to achieve better performance gains.
#AIreport
stateof.ai 2018

5.
Algorithm: An unambiguous specification of how to solve a particular problem.
Model: Once a ML algorithm has been trained on data, the output of the process is known as the model. This can
then be used to make predictions.
Supervised learning: This is the most common kind of (commercial) ML algorithm today where the system is
presented with labelled examples to explicitly learn from.
Unsupervised learning: In contrast to supervised learning, the ML algorithm has to infer the inherent structure of
the data that is not annotated with labels.
Transfer learning: This is an area of research in ML that focuses on storing knowledge gained in one problem and
applying it to a different or related problem, thereby reducing the need for additional training data and compute.
Good old fashioned AI: A name given to an early symbolic AI paradigm that fell out of favour amongst researchers
in the 1990s.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Definitions
#AIreport
stateof.ai 2018

7.
What is transfer learning and how does it relate to machine learning?
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Transfer Learning
Machine learning models are trained to solve a task by learning from examples. However, to solve a new and
different task, a trained model needs to be retained with new data specific to that task.
Transfer learning posits that knowledge acquired by a trained machine learning model can be re-applied (or
‘transferred’) during the training process for a new task.
#AIreport
stateof.ai 2018

17.
While in most cases, training on GPUs tends to outperform training on CPUs, the abundance of readily-available
CPU capacity in the datacenter makes it a useful and widely used platform.
At Facebook, for example, primary use case of GPUs is offline training rather than serving real-time data to
users.
While GPUs are used extensively for training, they’re not really needed for inference
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
AI hardware
Offline training uses a mix of GPUs and CPUs However, online training is CPU-heavy
#AIreport
stateof.ai 2018

31.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
However, detecting objects in images is not enough to produce real scene understanding
AI models make obvious mistakes when asked to describe a visual scene based on their understanding of objects
Image captioning helps expose the knowledge that computer vision systems learn by training on images labeled
with the objects they contain. Such computer vision models make seemingly obvious mistakes when attempting
to describe visual scenes. This suggests that having a common sense world model of objects and people is
required for an AI system to truly understand what's happening in a visual scene.
#AIreport
stateof.ai 2018

33.
Building datasets for teaching machine learning models to understand video
Enlist people to create videos that describe actions of interest, e.g. pretending to drop something off something
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
If a deep learning model can recognise and disambiguate nuanced actions from video, it should have internalised
common sense about the world. This is also called “intuitive physics”.
#AIreport
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35.
The “Generative Query Network” (GQN) can do this without human labels or domain knowledge, suggesting that
it captures the identities, positions, colors, and counts of objects in the scenes it observes.
If an ML system correctly predicts new viewpoints of the same scene, it has internalised knowledge of that scene
Machines can also understand visual scenes by learning to see from multiple viewpoints
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Examples of different scene viewpoints What the GQN observes and predicts vs. truth
#AIreport
stateof.ai 2018

37.
Strikingly, the more elegant AlphaZero system surpasses all other versions of AlphaGo (which is based on two
neural networks). AlphaZero achieves superhuman performance after 40 days of training.
AlphaZero showed that a deep RL system can learn from scratch to beat Go champions
AlphaZero is one neural network trained through self-play without human supervision or historical player data to
predict moves and chances of winning from a particular board position
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion #AIreport
stateof.ai 2018

38.
The agents each have their own neural networks trained through RL to yield long-term planning behavior in a
gameplay environment that is partially-observable and high-dimensional. That RL agents can collaborate in
teams to beat teams of humans is notable given the space of possible actions agents can take and the large
maps they can interact with.
OpenAI’s multi-agent RL system learns to play complex real-time strategy game, Dota2
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
OpenAI Five is a team of 5 agents that learn through RL-based self-play to optimize their gameplay policy
#AIreport
stateof.ai 2018

39.
Here, an RL agent learns optimal behaviors within a world model it imagined for itself
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
RL agents can also build their own world models and be trained within them
The agent observes the game environment, creates its own understanding of each frame (VAE), uses this
understanding to predict the next frame (MDN-RNN) and then trains its behavior to optimize a goal (C) in the
imagined environment.
Schematic for building a world model Using this world model allows an AI agent to perform at its best
#AIreport
stateof.ai 2018

40.
After many years of scandals, the research community is finally working to stem bias in ML models
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Fairness in machine learning: How do we ensure our models are not biased?
#AIreport
stateof.ai 2018

41.
Turkish is a gender-neutral language, yet Google Translate swaps the gender of the pronouns when translating
from English to Turkish and back to English
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
An example of biased machine learning systems: Stereotyping
#AIreport
stateof.ai 2018

45.
In computer vision, a model can show us which pixels it used to infer a specific label (e.g. which pixels = “dog”)
This way, we understand that the model has “learned” properly vs. predicted the right label for the wrong reason.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Explainability helps validate that ML models perform well for the “right” reasons
#AIreport
stateof.ai 2018

46.
Joint textual rationale generation and attention visualization provides deeper insight into decisions
For a given question and an image, the Pointing and Justification Explanation (PJ-X) model predicts the answer
and multimodal explanations which both point to the visual evidence for a decision and provide textual
justifications. Multimodal explanations results in better visual and textual explanations.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Next step: Justifying decisions in plain language and pointing to the evidence
#AIreport
stateof.ai 2018

47.
The more a feature is important, the greater the model’s prediction error as a result of the feature value change.
We can alter the value of a particular model feature to see how the overall model’s prediction error changes
Understanding feature importance gives us high level insight into a model’s behavior
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Features important for predicting cervical cancer
#AIreport
stateof.ai 2018

53.
Google’s AutoML automatically discovers the best model architecture for a computer vision task
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
AI to automate away AI engineers
AutoML traversed the architecture search space to find two new cell designs (Normal and Reduction, left
figueres) that could be integrated into a final model (NASNet, right graph) that outperformed all existing
human-crafted models.
#AIreport
stateof.ai 2018

54.
OpenMined: Train a model on lots of individual user devices such that their data never leaves their devices
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Distributed “federated” learning to decentralise data acquisition and model training
Large technology companies centralise immense amounts of user data. The community is now starting to push
back by creating tools to decentralise data ownership. In OpenMined, an AI model itself is encrypted by it’s
owner such that the user cannot steal it. User data stays locally on a user’s device and is accessed to update the
model’s parameters. These parameter changes from multiple users are aggregated back to the model owner for
updating.
#AIreport
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66.
“[At] DeepMind...the lab’s “staff costs” as it expanded to 400 employees totaled $138
million. That comes out to $345,000 an employee.”
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Demand: Anecdotally salaries continue to grow
“OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. It paid
another leading researcher, Ian Goodfellow, more than $800,000”. ‘I turned down
offers for multiple times the dollar amount I accepted at OpenAI,’Mr. Sutskever said.
‘Others did the same.’”
“Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less
education and just a few years of experience, can be paid from $300,000 to $500,000
a year or more in salary and company stock”
“Nick Zhang, president of the Wuzhen Institute...knows of experienced people getting
salary offers of $1 million or more to work at the AI research centres of Chinese
social-media giant Tencent or the web-services firm Baidu. ‘This was unimaginable five
years ago,’”
“Thomas Liang, a former executive at Chinese search giant Baidu estimates salaries in
the industry have roughly doubled since 2014”
#AIreport
stateof.ai 2018

68.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Diversity in machine learning
Diversity metrics for the industry are rarely publicised
➔ Key research labs are not yet making their workforce diversity statistics public.
➔ There are limited diversity stats for major machine learning conferences publicly available.
➔ For the largest machine learning conference by attendance, NIPS (Neural Information Processing
Systems), there is data available on a single dimension of diversity (gender) for the past few years*.
➔ For NIPS, the percentage of female attendees was 17% in 2017. This is lower than the technology
industry more generally (for example, 31% of Google employees are women and 20% of people in a
technical role at Google are women).
➔ The percentage of women attending NIPS has risen slightly over the past few years from 13% in
2015 to 17% in 2017.
➔ There are various initiatives aiming to increase diversity in machine learning:
*please let us know if you have similar statistics on other measures of diversity, such as race, that we can add to the report
#AIreport
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81.
4 year-old is leading the charge. It’s valued >$4.5B since raising $620M Series C+ in May 2018.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Population-level surveillance is taking off in China
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Government and defense
The Chinese government continue to roll out CCTV surveillance software based on computer vision. There are
170 million CCTV cameras as of late 2017. This network will grow to 400 million cameras in 3 years time.
#AIreport
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82.
In the US, companies including Google and Clarifai supplied AI technology to the Pentagon’s Project Maven
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Government and defense
In response, >4,500 Google employees signed a petition to quit if the company were to continue as a supplier
#AIreport
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84.
Massive data breaches such as Equifax’s heist of data about 146 million people has brought the privacy front of
mind in industry. In Europe, the General Data Protection Regulation has come into effect since May 25th 2018.
Companies must explicitly obtain consent from their users to access data for specific purposes and must allow
users to delete their records at will. This has driven work in differential privacy, on-device machine learning and
synthetic data creation to assuage privacy concerns of data systems. However, it’s unclear if consumers will
change their behavior as a result.
Why now?
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Privacy preservation and data anonymisation
#AIreport
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90.
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Cybersecurity
● Insider threat detection: Applying machine learning to large amounts of data on employee behaviour
reduces the time to flag potential malicious intent.
Selected examples:
Where and how is machine learning being used effectively?
● Network and endpoint security: Supervised learning is used to detect malicious activity on an
organisation’s network based on data from past attacks. Unsupervised learning is used to automatically
learn what is normal and what is abnormal within a network on a an ongoing basis.
Selected examples:
#AIreport
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91.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Why now?
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Warehouse automation
eCommerce growth decreases order size for item picking in warehouses and increases customer expectations
around the speed of fulfilment. Warehouse space and labour are both scarce driving more use of robotics.
Retailers are also reacting to Amazon’s investment in this area following their acquisition of Kiva.
Number of robots working in Amazon fulfilment centres
% of warehouse and logistics managers reporting
inability to find hourly workers as a top concern
#AIreport
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97.
The world population is expected to grow from 7.6 billion to 9.6 billion by 2050. We need to produce 70% more
food calories to feed the world’s population by then. Robotics, control systems, connected devices in fields and
greenhouses and new methods of farming must be developed to fill this food production gap.
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Agriculture: Indoor and outdoor farming
Why now?
The need for boosting food production Farms are investing in technology now
#AIreport
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99.
● Health inspection for crops and animals: Use computer vision and wearable sensors to learn models of
plant and animal health and use them to detect anomalies.
● Greenhouse control systems: Use native sensors and actuators in greenhouses to collect data on growing
conditions, learn a dynamic climate model and use it to optimise crop yield and energy consumption.
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Agriculture: Indoor and outdoor farming
● Vertically-integrated farming: Compact, self-contained greenhouses for growing crops closer to the point
of consumption. The farms have their climates that can be operated using similar ML-driven control
systems.
Selected examples:
Where and how is machine learning being used effectively?
Selected examples:
Selected examples:
#AIreport
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105.
● Credit/Loans: The cost of calculating and underwriting risk is improved through automation and the
discovery of novel features through machine learning that improve the overall efficiency of this process.
Peer to peer lending has also benefited from these drivers.
● Wealth management: Software-driven automation of capital management, portfolio construction and tax
optimisation. These services materially reduce the fees for consumers to invest their long-term savings.
Introduction | Research | Talent | Industry | Politics | Predictions | ConclusionIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Finance
Selected examples:
Where and how is machine learning being used effectively?
Selected examples:
● Fraud prevention: Using both supervised and unsupervised learning to detect known and novel fraudulent
behaviors in electronic transactions, interpersonal communications, and claims images.
Selected examples:
#AIreport
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106.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Why now?
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Enterprise automation
Reducing operational process cost and complexity through software-defined automation is now a Board-level
priority in the enterprise. Manual processes are prone to costly errors, do not scale, are difficult to track and
troubleshoot, and make organisations slow to respond to younger and more nimble new entrants.
% of average week spent on tasks % of day spent in different modes of work
⅔ day is unstructured
or unpredictable
⅓ day is structured,
predictable, automated
or automatable
#AIreport
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108.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Why now?
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Material science
An enormous amount of experimental data has been generated on the properties of materials. Progress in
materials science is a multiplier on broader engineering progress. But most materials are still found empirically,
which limits the rate of progress. For example, scientists have manually investigated 6,000 combinations of
ingredients that form metallic glass over the past 50 years.
Where and how is ML being used effectively?
Similar its application in drug discovery, machine learning can be used to learn the rules of material science
discovery. For example, models can learn the structure of molecules and/or the stepwise process of efficiently
testing these molecular properties. By using these techniques, researchers at Stanford Synchrotron Radiation
Lightsource were able to create and screen 20,000 combinations of ingredients that form metallic glass in a
single year. That’s research and development sped up by 167x!
Selected examples:
#AIreport
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110.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Public Attitudes to Automation: Two Surveys
Pew Research Center: Americans and Automation in Everyday Life
Brookings survey: Attitudes to AI
We will review selected results from two major surveys of attitudes to AI and automation in the U.S.
➔ Conducted May 1-15 2017. Published October 2017.
➔ Survey of 4135 US adults
➔ Recruited from landline and cellphone random-digit-dial surveys
➔ Conducted May 9-11 2018. Published May 2017.
➔ Survey of 1535 adult internet users in the U.S.
➔ Recruited through the Google Surveys platform. Responses were weighted using gender,
age, and region to match the demographics of the national internet population as
estimated by the U.S. Census Bureau’s Current Population Survey
#AIreport
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111.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Public Attitudes to Automation: Pew Research Center
Growing awareness of automation impacting jobs
“18% of Americans
indicate that they
personally know
someone who has lost a
job, or had their pay or
hours reduced, as a
result of workforce
automation”
#AIreport
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126.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
How is the US labour market actually changing?
Since 2010 there has been a marked change in how long unemployment lasts for
#AIreport
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130.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
How much of this is due to automation?
It’s hard to say for now. There are many confounding factors including globalisation/offshoring, reduced
unionisation, increased financialisation of the economy, increased consolidation, and demographic shifts.
There are two poles of thought on how machine learning will affect the labour market:
➔ “Don’t worry” - Historically technology has been a net job creator and it won’t be different this time.
Machine learning will create more jobs than it destroys and like previous industrial revolutions, most
of those jobs will be new ones that we can’t imagine today. Yes, we got Automated Teller Machines at
banks, but we also got many new jobs that replaced the bank teller jobs that were lost.
➔ “Worry” - This time it’s different. In previous industrial revolutions we automated human muscular
power and somewhat routine cognitive skills. With increasingly advanced machine learning we will
replicate more and more of human intelligence, reducing the number of well paid jobs and adding
fewer jobs than are destroyed.
#AIreport
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132.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
It is also still early, there are only 2 million industrial robots in the world
Install base growing 12% year-on-year
#AIreport
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135.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
One recent piece of analysis found that while Amazon is rapidly hiring people and robots,
taken as a whole retail is losing jobs
#AIreport
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136.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
If automation does reduce net employment and/or wages what new policies will emerge?
Universal Basic Income (UBI) or Basic Income
➔ Has received substantial media coverage over the past years. We review various trials that are now
being rolled out
Universal Basic Services (UBS)
➔ A less mainstream idea that was recently fleshed out by the Institute for Global Prosperity at UCL. We
highlight the proposal as an interesting new alternative or complement to UBI.
#AIreport
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137.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
Basic Income trials roll out
‘Basic Income’ aims to mitigate technological unemployment with guaranteed payments to cover basic needs
➔ Finland’s basic income trial is running with 2,000 randomly selected participants receiving €560 per
month. Will conclude in December 2018. Analysis of the effects will take place in 2019.
➔ Ontario basic income pilot began enrolling participants in April 2018. Will be restricted to 4,000 lower
income participants.
➔ Five municipal experiments in the Netherlands with basic income commenced in late 2017.
➔ Barcelona launched B-MINCOME experiment in October 2017 with 2000 low income households.
➔ US Charity GiveDirectly launched trial in Kenya in November 2017. More than 21,000 people will
eventually receive some type of cash transfer, with more than 5,000 receiving a long-term basic
income.
➔ Y Combinator research published proposal for randomised control trial with 3000 adults in the United
States
#AIreport
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139.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
AI Nationalism: flurry of National AI strategies announced
China 2030 (announced July 2017)
➔ Partly a reaction to Obama White House report on AI (in 2016)
➔ New state funded $2.1 billion AI park in Beijing
➔ Call for researchers to be making major breakthroughs by 2025
➔ By 2030, China will “become the world’s premier artificial intelligence
innovation center and foster a new national leadership and establish
the key fundamentals for an economic great power.”
➔ Baidu announces new lab in collaboration with Chinese government
➔ Goal: to build a $150 billion AI industry by 2030
#AIreport
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140.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
AI Nationalism: flurry of National AI strategies announced
French AI Strategy (announced March 2018)
➔ “My goal is to recreate a European sovereignty
in AI” - Macron
➔ €1.5 billion committed over 5 years
➔ New AI research centres in Paris opened by
Facebook, Google, Samsung, DeepMind, Fujitsu
➔ Plan to open up of data collected by
state-owned organizations such as France’s
centralized healthcare system
➔ Separately, France announces that foreign
takeovers of AI companies will be subject to
government approval
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152.
8 predictions for the next 12 months
1. A lab located in China makes a significant research breakthrough.
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion
2. DeepMind has a breakthrough result successfully applying RL to learn how to play Starcraft.
3. Deep learning continues to dominate the discussion without major alternatives appearing.
4. The first therapeutic drug discovered using machine learning produces positive results in trials.
6. The government of an OECD country blocks the acquisition of a leading machine learning company (defined
as valuation >$100m) by a US or Chinese headquartered technology company.
7. Access to Taiwanese and South Korean semiconductor companies becomes an explicit part of the trade war
between America and China.
5. Chinese and American headquartered technology companies make acquisitions of machine learning
companies based in Europe totalling over $5b.
#AIreportIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
8. A major research institution “goes dark” by refraining from publishing key work in the open due to
geopolitical concerns.
stateof.ai 2018

154.
Thanks!
Congratulations on making it to the end! Thanks for reading.
In this report, we set out to capture a snapshot of the exponential progress in the field of machine learning, with a
focus on developments in the past 12 months. We believe that AI will be a force multiplier on technological
progress in our world, and that wider understanding of the field is critical if we are to navigate such a huge
transition.
We tried to compile a snapshot of all the things that caught our attention in the last year across the range of
machine learning research, commercialisation, talent and the emerging politics of AI.
Thanks to Mary Meeker for the inspiration.
We would appreciate any and all feedback on how we could improve this report further. Thanks again for reading!
Nathan Benaich (@nathanbenaich) and Ian Hogarth (@soundboy)
#AIreportIntroduction | Research | Talent | Industry | Politics | Predictions | Conclusion
stateof.ai 2018

155.
The authors declare a number of conflicts of interest as a result of being investors and/or advisors, personally or
via funds, in a number of private and public companies whose work is cited in this report. This concerns the
following companies:
Startups
GTN.ai, TwentyBN, Kheiron Medical, Accelerated Dynamics, Avidbots, Optimal Labs, Ravelin, Tractable, LabGenius,
and Mapillary.
Public companies
Alphabet, NVIDIA, Facebook, Microsoft, Intel, Baidu, Amazon, and Alibaba.
Conflicts of interest
Introduction | Research | Talent | Industry | Politics | Predictions | Conclusion #AIreport
stateof.ai 2018